Developing an Urban Visualization System for Virtual Environments using Computer Graphics

(TUBITAK CAREER Award)

We developed an urban visualization framework for the population and visualization of complex
urban environments (see Figure 1). To this end, we develop a novel data structure for the storage and
representation of buildings in an urban environment, called slicewise representation,
which reduces the computational and storage costs of the modeling, visualization and rendering
of urban environments; an occlusion culling algorithm, which speeds up the visualization of city
models during walkthrough in these environments. The slice-wise representation is based on the
observation that the visible parts of the buildings in a typical urban walkthrough are
mostly in one of the following three cases (see Figures 2 and 3). Our occlusion culling algorithm
is a conservative algorithm based on occluder shrinking. Figure 4 illustrates the shrinking method
we used applied on a three dimensional object.

Figure 1: Overview of the urban visualization framework: in the first phase, we
read the scene and calculate the bounding boxes of objects. Next, we apply uniform
subdivision to each object. Then, we cluster the cells of the uniform subdivision
into slices. After creating the shrunk versions of the objects, these slices are
checked for occlusion and a tight visible set is determined for each grid location.
The phases in dashed blocks are performed in the preprocessing phase.
The view-frustum culling (VFC) is also done during navigation.

Figure 2: Visibility forms during urban navigation: (a) L-shaped form; (b) vertical rectangular form;
(c) horizontal rectangular form. In each part of the figure, the visible part of the occludee is the
green transparent area.
Figure 3: Illustration of the visibility forms during a typical urban walkthrough.

We propose techniques for the monoscopic and stereoscopic visualization of urban
environments. To speed up the stereoscopic visualization, we propose to generate the view for
one eye using the view for the other, instead of generating the two views separately. We also
exploited the Graphics Processor Unit and the related data structures, such as Vertex Buffer Objects,
to speed up the visualization process (see Figure 5).

Figure 5: The VBO data structure used in GPU-based visualization.
The object triangles are constructed using the index buffers created
in the GPU and accessed as needed for each building and for each
slice.

We model the crowds in urban environments using a model-based approach where each agent
can show complex behaviors based on its skeletal motion. We use occlusion culling to
eliminate the agents that are occluded by the buildings and view frustum culling to eliminate
agents that are out of the view frustum for fast rendering of crowds. We also use different
levels of details of the agents to render the agents according to the distance from the
viewpoint. We also work on approaches for global and local path planning for the behavior of
agents in crowds. We model the spreading of the panic behavior throughout the crowd. We
propose techniques to simulate the behavior of agents for normal and emergency situations,
such as fire, explosion, and terrorist attacks, in outdoor environments (see Figure 6).
We also study the effects of personality traits to the crowd behavior for indoor environments,
such as museums, exhibition galleries (see Figures 7 and 8).

The developed techniques and the proposed framework can be used in urban planning,
military simulations, geographical information systems, tourism, entertainment industry,
including the film sector and computer games, and security applications.